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a-r-d/hold-tlt-version.py

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QuantConnect - simple MACD strategy against SPY, 50/150 day cross, long and short, leverage is none
Raw
hold-tlt-version.py
import numpy as np
### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2005,1,1) #Set Start Date
self.SetEndDate(2018,2,1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
self.SetWarmUp(150) #warm u for 100 days
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
#self.sizing = startCash * 0.02
# Find more symbols here: http://quantconnect.com/data
# self.SetBenchmark("NFLX")
self.AddEquity("SPY", Resolution.Daily)
self.AddEquity("TLT", Resolution.Daily)
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
self.longOnly = True
self.load_symbols()
for symbol in self.symbols:
symbol.weight = 0
symbol.stopprice = None
symbol.lastSignal = "NA"
# https://github.com/Quantconnect/Lean/blob/master/Algorithm.Python/MACDTrendAlgorithm.py1
# 72, 189, 9, VS 10,100,5 vs 50, 150, 9 --- seems that best are short is 1/2 to 1/4 ratio of the long
symbol.macd = self.MACD(symbol, 50, 150, 9, MovingAverageType.Simple, Resolution.Daily)
# trade every day 30 minutes after open
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), Action(self.trade))
def OnData(self, data):
pass
def trade(self):
for symbol in self.symbols:
if not symbol.macd.IsReady:
continue
macd = symbol.macd
tolerance = 0.0025;
holdings = self.Portfolio[symbol].Quantity
signal = macd.Signal.Current.Value
fast = macd.Fast.Current.Value
slow = macd.Slow.Current.Value
current = macd.Current.Value
numHoldings = len(self.symbols)
tradeQty = 1.25 / numHoldings
if holdings <= 0 and fast > slow: # 0.01%
symbol.lastSignal = 'LONG'
#self.Debug("MACD signal long:" + str(symbol) + " signal:" + str(signal) + " slow:" + str(slow) + " fast:" + str(fast))
self.SetHoldings(symbol, tradeQty)
self.Liquidate("TLT")
elif holdings >= 0 and slow > fast:
symbol.lastSignal = 'SHORT'
#self.Debug("MACD signal short: signal: " + str(signal))
if not self.longOnly:
self.SetHoldings(symbol, -1 * tradeQty)
else:
self.Liquidate(symbol)
self.SetHoldings("TLT", 1)
def load_symbols(self):
syl_list = [
#'SPY' #, 'USO', 'GLD', 'SLV', 'VNQ', 'HYG', 'EWJ'
#'CAT', 'DE', 'CVX', 'LMT', 'HON', 'GM'
#'IBB', 'SPY', 'IYR', 'IYF', 'IYH', 'IYM'
'SPY'
]
self.symbols = []
for i in syl_list:
self.symbols.append(self.AddEquity(i, Resolution.Daily, Market.USA, True, 1.5).Symbol)
Raw
main.py
import numpy as np
### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2005,1,1) #Set Start Date
self.SetEndDate(2018,2,1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
self.SetWarmUp(150) #warm u for 100 days
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
#self.sizing = startCash * 0.02
# Find more symbols here: http://quantconnect.com/data
# self.SetBenchmark("NFLX")
self.AddEquity("SPY", Resolution.Daily)
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
self.longOnly = False
self.load_symbols()
for symbol in self.symbols:
symbol.weight = 0
symbol.stopprice = None
symbol.lastSignal = "NA"
# https://github.com/Quantconnect/Lean/blob/master/Algorithm.Python/MACDTrendAlgorithm.py1
# 72, 189, 9, VS 10,100,5 vs 50, 150, 9 --- seems that best are short is 1/2 to 1/4 ratio of the long
symbol.macd = self.MACD(symbol, 50, 150, 9, MovingAverageType.Simple, Resolution.Daily)
# trade every day 30 minutes after open
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), Action(self.trade))
def OnData(self, data):
pass
def trade(self):
for symbol in self.symbols:
if not symbol.macd.IsReady:
continue
macd = symbol.macd
tolerance = 0.0025;
holdings = self.Portfolio[symbol].Quantity
signal = macd.Signal.Current.Value
fast = macd.Fast.Current.Value
slow = macd.Slow.Current.Value
current = macd.Current.Value
numHoldings = len(self.symbols)
tradeQty = 0.99 / numHoldings
if holdings <= 0 and fast > slow: # 0.01%
symbol.lastSignal = 'LONG'
#self.Debug("MACD signal long:" + str(symbol) + " signal:" + str(signal) + " slow:" + str(slow) + " fast:" + str(fast))
self.SetHoldings(symbol, tradeQty)
elif holdings >= 0 and slow > fast:
symbol.lastSignal = 'SHORT'
#self.Debug("MACD signal short: signal: " + str(signal))
if not self.longOnly:
self.SetHoldings(symbol, -1 * tradeQty)
else:
self.Liquidate(symbol)
def load_symbols(self):
syl_list = [
#'SPY' #, 'USO', 'GLD', 'SLV', 'VNQ', 'HYG', 'EWJ'
#'CAT', 'DE', 'CVX', 'LMT', 'HON', 'GM'
#'IBB', 'SPY', 'IYR', 'IYF', 'IYH', 'IYM'
'SPY'
]
self.symbols = []
for i in syl_list:
self.symbols.append(self.AddEquity(i, Resolution.Daily, Market.USA, True, 1.0).Symbol)
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